Dataset Information

Selective Intensive Care Unit Admission After Adult Supratentorial Tumor Craniotomy: Complications, Length of Stay, and Costs.

ABSTRACT:

Background

Admitting patients to an intensive care or medium care unit (ICU/MCU) after adult supratentorial tumor craniotomy remains common practice even though some studies have suggested lower level care is sufficient for selected patients. We have introduced a "no ICU, unless" policy for tumor craniotomy patients.

Objective

To provide a quieter postoperative environment for patients, reduce the burden on the ICU department, and to evaluate whether costs can be reduced.

Methods

A cohort study was performed comparing patients that underwent tumor craniotomy for supratentorial tumors during 1 yr after introduction (n = 109) of the new policy with the year before (n = 107). Rate of complications was evaluated, as was the length of stay and patient satisfaction using qualitative evaluation. Finally, costs were evaluated comparing the situation before and after implementation of the new protocol.

Results

A reduction in ICU/MCU admittance from 64% to 24% of patients was found resulting in 13.3% cost reduction (€1950 per case), without increasing the length of stay at the ward. The length of stay in the hospital was similar. Complications were significantly reduced after implementing the new policy (0.98 vs 0.53 per patient, P = .003). Patients that were interviewed after the new policy reported feeling safe and at ease at the ward.

Conclusion

Changing our policy from "ICU, unless" to "no ICU, unless" reduced complication rates and length of stay in the hospital while keeping patients satisfied. Hospital costs related to the admission have been significantly reduced by the new policy.

SUBMITTER: Laan MT

PROVIDER: S-EPMC6911731 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIntensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared.MethodsFrom calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established.ResultsThe ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics.ConclusionsInference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no "one best model"; thus, ICU "performance" is determined by model choice and any rankings thereupon should be circumspect.